2017/11/28 by Xiaojie Jin, Jin, Xiaojie, Yingzhen Yang +12
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Neural and Evolutionary Computing (cs.NE) #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #cs.CV #cs.NE #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1711.10067
To appear at ICML 2018
openalex publication_date 2017/11/28 · arxiv created 2018/05/22 · arxiv updated 2018/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via ad hoc processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces parameter sharing throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to 180 times smaller and theoretically up to 16 times faster than the well-established baselines, without noticeable performance drop.